07-02: Exercises — CSV Files¶

Notes reference: 07-02: Working with CSV Files

Q1: Write a CSV file¶

Write a CSV with columns name, city, score for five students.

Solution

import csv

header = ["name", "city", "score"]
data = [
    ["Rahul",   "Dhaka",    88.5],
    ["Sara",    "New York", 92.0],
    ["James",   "Berlin",   79.3],
    ["Nadia",   "Tokyo",    95.1],
    ["Michael", "Nairobi",  81.7],
]

with open("students.csv", "wt", encoding="utf-8", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(header)
    writer.writerows(data)
print("students.csv written.")

Q2: Read a CSV with csv.reader¶

Read students.csv and print each row (skip header).

Solution

import csv

with open("students.csv", "rt", encoding="utf-8", newline="") as f:
    reader = csv.reader(f)
    header = next(reader)
    print("Columns:", header)
    for row in reader:
        name, city, score = row
        print(f"{name} from {city} — score: {score}")

Q3: DictReader — read as dictionaries¶

Read students.csv using DictReader and print each student's name and score.

Solution

import csv

with open("students.csv", "rt", encoding="utf-8", newline="") as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(f"{row['name']}: {row['score']}")

Q4: Type conversion — CSV values are strings¶

Load students.csv and compute the average score (scores must be converted to float).

Solution

import csv

with open("students.csv", "rt", encoding="utf-8", newline="") as f:
    reader = csv.DictReader(f)
    scores = [float(row["score"]) for row in reader]

avg = sum(scores) / len(scores)
print(f"Average score: {avg:.2f}")

Q5: Filter and save¶

Read students.csv and write only students with score >= 85 to high_scorers.csv.

Solution

import csv

with open("students.csv", "rt", newline="") as infile, \
     open("high_scorers.csv", "wt", newline="") as outfile:

    reader = csv.DictReader(infile)
    writer = csv.DictWriter(outfile, fieldnames=reader.fieldnames)
    writer.writeheader()
    for row in reader:
        if float(row["score"]) >= 85:
            writer.writerow(row)

print("high_scorers.csv created.")

Q6: Append a row¶

Append a new student record to students.csv.

Solution

import csv

new_student = ["Amina", "Chittagong", 90.4]

with open("students.csv", "at", encoding="utf-8", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(new_student)
print("New student added.")

Q7: DictWriter — write from dicts¶

Write a product catalogue CSV from a list of dictionaries.

Solution

import csv

products = [
    {"product": "Laptop",     "price_bdt": 85000, "stock": 12},
    {"product": "Headphones", "price_bdt": 3500,  "stock": 45},
    {"product": "Keyboard",   "price_bdt": 1800,  "stock": 30},
]

with open("products.csv", "wt", encoding="utf-8", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=["product", "price_bdt", "stock"])
    writer.writeheader()
    writer.writerows(products)
print("products.csv written.")

Q8: Custom delimiter¶

Write a pipe-delimited (|) version of students.csv.

Solution

import csv

with open("students.csv", "rt", newline="") as infile, \
     open("students_pipe.txt", "wt", newline="") as outfile:

    reader = csv.reader(infile)
    writer = csv.writer(outfile, delimiter="|")
    for row in reader:
        writer.writerow(row)
print("Pipe-delimited file written.")

Q9: Group and aggregate¶

Read students.csv and compute the average score per city.

Solution

import csv
from collections import defaultdict

city_scores = defaultdict(list)

with open("students.csv", "rt", newline="") as f:
    reader = csv.DictReader(f)
    for row in reader:
        city_scores[row["city"]].append(float(row["score"]))

for city, scores in sorted(city_scores.items()):
    avg = sum(scores) / len(scores)
    print(f"{city}: {avg:.1f} (n={len(scores)})")

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